Stable Dynamic Optimization Strategies for Large-Scale Chemical Processes

大规模化学过程的稳定动态优化策略

• 批准号: 9729075
• 负责人: Lorenz Biegler
• 金额: $ 23.69万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-01-15 至 2000-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9729075&HistoricalAwards=false
• 关键词: Stable; Dynamic; Optimization; Strategies; Large

Biegler CTS-9729075 Strategies for process optimization have seen widespread applications over the past decade. These have been applied in the design of new chemical processes, real-time optimization of process units and plants in petrochemical processes, and for sophisticated analyses related to the operability and flexibility of chemical processes. The vast majority of these applications have been for steady state process models, described by systems of algebraic equations. Simultaneously, development of powerful, large-scale dynamic simulation tool are also becoming widespread and equation based dynamic process models have been constructed with up to 100,000 differential-algebraic equations (DAE). The success of dynamic process simulation has led to the demand for optimization tools that deal readily with process models described by differential algebraic equations (DAEs). However, development of dynamic optimization strategies has lagged that of simulation for a number of reasons. Among these are conceptual limitations of current optimization strategies. These become important for DAE models that are highly constrained or have unstable dynamic modes. The latter are often true in reactive and reactive-separation systems including exothermic reactors and reactive distillation. The PI is planning on developing a simultaneous formulation for dynamic optimization that includes a stable large-scale decomposition based on boundary value formulations. Novel nonlinear programming, strategies that detect unstable modes, exploit problem structure for large scale decomposition and adapt leading, edge methods for the treatment of large sets of inequality constraints will be considered. The development of these strategies will be aided by research collaborations with applied mathematicians that specialize in nonlinear programming and DAE solution algorithms. The combination of these strategies will lead to large-scale dynamic optimization strategies that can be applied to dynamic simulation models currently considered in industrial applications. The methods developed will be validated by large scale industrial applications in reaction engineering and reactive distillation.

Biegler CTS-9729075 流程优化策略在过去十年中得到了广泛应用。 这些已应用于新化学工艺的设计、石化工艺中工艺装置和工厂的实时优化，以及与化学工艺的可操作性和灵活性相关的复杂分析。 这些应用中的绝大多数都是稳态过程模型，由代数方程组描述。 同时，强大的大规模动态仿真工具的开发也越来越广泛，并且已经使用多达 100,000 个微分代数方程 (DAE) 构建了基于方程的动态过程模型。 动态过程仿真的成功引发了对能够轻松处理微分代数方程 (DAE) 描述的过程模型的优化工具的需求。 然而，由于多种原因，动态优化策略的发展落后于模拟。 其中包括当前优化策略的概念限制。 这些对于高度约束或具有不稳定动态模式的 DAE 模型变得非常重要。 后者通常适用于反应性和反应性分离系统，包括放热反应器和反应性蒸馏。 PI 正计划开发一种用于动态优化的联立公式，其中包括基于边界值公式的稳定大规模分解。 将考虑新颖的非线性规划、检测不稳定模式的策略、利用问题结构进行大规模分解以及采用前沿方法来处理大量不等式约束。 这些策略的开发将得到与专门从事非线性规划和 DAE 求解算法的应用数学家的研究合作的帮助。 这些策略的结合将产生大规模的动态优化策略，可应用于当前工业应用中考虑的动态仿真模型。 所开发的方法将通过反应工程和反应蒸馏中的大规模工业应用进行验证。